Dependencies of the CUDA-Python bindings and some versions that are known to work are as follows:
- Driver: Linux (450.80.02 or later) Windows(456.38 or later)
- CUDA Toolkit 11.0 to 11.4 - e.g. 11.4.48
- Cython - e.g. 0.29.21
- Versioneer - e.g. 0.20
To compile the extension in-place, run:
python setup.py build_ext --inplace
To compile for debugging the extension modules with gdb, pass the --debug
argument to setup.py.
You can use
python setup.py develop
to use the module in-place in your current Python environment (e.g. for testing of porting other libraries to use the binding).
conda env create -f docs/environment-docs.yml
conda activate cuda-python-docs
Then compile and install cuda-python following the steps above.
cd docs
make html
open build/html/index.html
conda env create -f docs_src/environment-docs.yml
conda activate cuda-python-docs
Then compile and install cuda-python following the steps above.
cd docs_src
make html
open build/html/index.html
git checkout gh-pages
cd docs_src
make html
cp -a build/html/. ../docs/
Dependencies of the test execution and some versions that are known to work are as follows:
- numpy-1.19.5
- numba-0.53.1
- matplotlib-3.3.4
- scipy-1.6.3
- pytest-benchmark-3.4.1
You can run the included tests with:
pytest
You can run the included tests with:
pytest examples
You can run benchmark only tests with:
pytest --benchmark-only
The included examples are:
examples/extra/jit_program.py: Demonstrates the use of the API to compile and launch a kernel on the device. Includes device memory allocation / deallocation, transfers between host and device, creation and usage of streams, and context management.examples/extra/numba_emm_plugin.py: Implements a Numba External Memory Management plugin, showing that this CUDA Python Driver API can coexist with other wrappers of the driver API.